AI 中文总结
研究提出TRACE-PCa模型，无需病变分割，通过预训练3D MRI模型编码序列扫描，引入时间注意力门，融合临床变量，在纵向AS队列验证中表现出色，能减少不必要活检，有预测前列腺癌进展的潜力。
AI 中文摘要
主动监测（AS）是低风险前列腺癌的首选策略，但当前方案依赖定期重复活检，其中大部分显示无进展且不必要。现有风险分层工具基于单时间点成像或依赖明确病变分割，限制了捕捉纵向变化的能力并排除了无MRI可见病变的患者。本研究提出一种无需病变分割即可预测AS期间病理进展的端到端时间和多模态模型。用预训练的3D MRI基础模型对每次序列扫描进行编码，并引入时间注意力门重新校准多次就诊特征以放大与进展相关的局部成像变化。然后在多模态框架中将门控成像表示与临床变量融合以估计进展概率。在纵向AS队列上验证，该方法始终优于竞争基线，与代表当前临床实践的放射科医生评估相当。它保持高阴性预测值同时实现更高阳性预测值，证明其在监测期间安全减少不必要活检的潜力。
英文摘要
Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary. Existing risk-stratification tools operate on single time-point imaging or depend on explicit lesion segmentation, limiting their ability to capture longitudinal change and excluding patients without an MRI-visible lesion. In this study, we propose an end-to-end temporal and multimodal model for predicting pathological progression during AS without lesion segmentation. We encode each serial scan with a pretrained 3D MRI foundation model and introduce a temporal attention gate that recalibrates the multi-visit features to amplify focal imaging changes associated with progression. The gated imaging representation is then fused with clinical variables in a multimodal framework to estimate the probability of progression. Validated on a longitudinal AS cohort, our approach consistently outperforms competing baselines and performs comparably to the radiologist assessment representing current clinical practice. It maintains high negative predictive value while achieving higher positive predictive value, demonstrating its potential to safely reduce unnecessary biopsies during surveillance.
Comments7 pages, 4 figures
